The paradigm of worst-group loss minimization has shown its promise in
avoiding to learn spurious correlations, but requires costly additional
supervision on spurious attributes. To resolve this, recent works focus on
developing weaker forms of supervision -- e.g., hyperparameters discovered with
a small number of validation samples with spurious attribute annotation -- but
none of the methods retain comparable performance to methods using full
supervision on the spurious attribute. In this paper, instead of searching for
weaker supervisions, we ask: Given access to a fixed number of samples with
spurious attribute annotations, what is the best achievable worst-group loss if
we "fully exploit" them? To this end, we propose a pseudo-attribute-based
algorithm, coined Spread Spurious Attribute (SSA), for improving the
worst-group accuracy. In particular, we leverage samples both with and without
spurious attribute annotations to train a model to predict the spurious
attribute, then use the pseudo-attribute predicted by the trained model as
supervision on the spurious attribute to train a new robust model having
minimal worst-group loss. Our experiments on various benchmark datasets show
that our algorithm consistently outperforms the baseline methods using the same
number of validation samples with spurious attribute annotations. We also
demonstrate that the proposed SSA can achieve comparable performances to
methods using full (100%) spurious attribute supervision, by using a much
smaller number of annotated samples -- from 0.6% and up to 1.5%, depending on
the dataset.Comment: ICLR 2022 camera read